Safety Advancing Federated Estimation of Risk using AI (SAFER AI)

使用人工智能推进安全联合风险估计 (SAFER AI)

• 批准号: 10093091
• 金额: $ 148.3万
• 依托单位: THE DATA ANALYSIS BUREAU LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10093091
• 关键词: Safety; Advancing; Federated; Estimation; Risk

Data privacy and security is a key concern for the adoption of Artificial Intelligence/Machine Learning with IoT (Internet of Things), particularly where that data is personal or security sensitive. The number of IoT-enabled devices and machines is growing exponentially, estimated to reach 26Bn globally by 2030\. Creating platforms optimised to maximise private and secure machine learning at the edge in distributed systems that can be trusted is an urgent priority.The consortium's mission is to help solve the challenge of implementing trustworthy AI-in-IoT. This will be achieved by accelerating the development of a federated, secure, privacy-preserving, and auditable AI-for-IoT platform optimised for machine learning in IoT and edge systems. OctaiPipe is a first-of-its-kind innovation that combines privacy-preserving machine learning technology, cyber security, continuous collaborative learning and AI lifecycle management. This will allow IoT-enabled businesses to build, deploy, and manage machine learning software that guarantees the privacy and security of device data and its use, allowing the user to have a high degree of trust in the AI solutions embedded in them.Many organisations already collect high-level operational and HSE incident data intelligence through various Industrial IoT devices and cameras to successfully predict safety incidents. However, analytics based on this is not meaningfully actionable to drive changes that preventatively reduce risks.For predictions to be meaningfully actionable, they must be made at a sufficient level of granularity within the workgroup. Technology now exists to monitor such events at granularity---enabling the build of models to predict and forecast events in the future at sufficient granularity---enabling game-changing preventative impact at the workgroup level.Fortunately, HSE critical events are rare within single sites or even organisations---meaning insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented. However, progress towards an AI-enabled solution is impeded by:a) a requirement for more observations than one organisation can generate alone, so it is imperative to share data, andb) barriers to sharing data across organisations that, until now, have not been overcome.Federated Learning solves this. The project will enable organisations to combine data to facilitate actionable incident predictions for small work groups.This project aims to address vulnerabilities in Machine Learning for IoT with a specific focus on FL and addresses the fundamental challenges of socially responsible AI adoption into society.

数据隐私和安全性是通过物联网（物联网）采用人工智能/机器学习的关键问题，尤其是在该数据对个人或安全敏感的情况下。支持IOT的设备和机器的数量呈指数增长，估计到2030年\ \ 2030 \。创建优化的平台以最大化可以信任的分布式系统的边缘的私人和安全的机器学习是一个紧迫的优先事项。该联盟的使命是帮助解决实施可信赖的AI-In-Iniot的挑战。这将通过加快联合，安全，保护隐私和可审核的AI-for-for AI-of-iot平台的开发来实现。 Octaipipe是一种首要创新，结合了保护隐私的机器学习技术，网络安全，持续的协作学习和AI生命周期管理。这将允许IOT启用的企业构建，部署和管理机器学习软件，以确保设备数据及其使用的隐私和安全性，从而使用户可以高度信任嵌入其中的AI解决方案。许多组织已经通过各种工业IOT IOT Devices and Cameras to Safficess收集了高级操作和HSE事件，并将其HSE事件进行成功。但是，基于此的分析对于驱动预防性降低风险的变化并不是有意义的。为了使预测具有有意义的行动，必须在工作组内的足够水平的粒度水平进行。 Technology now exists to monitor such events at granularity---enabling the build of models to predict and forecast events in the future at sufficient granularity---enabling game-changing preventative impact at the workgroup level.Fortunately, HSE critical events are rare within single sites or even organisations---meaning insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented.但是，朝着基于AI的解决方案方面的进展受到：a）比一个组织可以单独产生的观察结果的要求，因此必须共享数据，并且B）在迄今尚未克服的组织之间共享数据的障碍。FederatedLearning对此进行了解决。该项目将使组织能够组合数据，以促进针对小型工作组的可行事件预测。该项目旨在解决物联网机器学习中的脆弱性，并针对FL的特定关注，并应对社会负责的AI对社会的基本挑战。